This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image in the collection and train a discriminative model to predict their relative position within the image. We argue that doing well on this task will require the model to learn about the layout of visual objects and object parts. We demonstrate that the feature representation learned using this within-image context prediction task is indeed able to capture visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned features, when used as pre-training for the R-CNN object detection pipeline, provide a significant boost over random initialization on Pascal object detection, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations.

该研究探讨了利用空间上下文作为丰富的视觉表征的监督信号的方法。通过从每个图像中提取随机配对的路径来训练卷积神经网络，用于预测第二个路径相对于第一个路径的位置，从而实现对目标的识别，并证明该方法在性能上优于其他算法。

无监督视觉表示学习：基于上下文预测